Field
The technology disclosed herein relates to image processing, and in particular, to techniques for super resolution techniques that account for global and local motion.
Description of the Related Art
A number of technologies rely on image data to accomplish their goals. For example, medical diagnostics, surveillance, forensic and satellite imaging applications make heavy use of imaging data. Efficacy of technologies that rely on imaging data can be improved by improving the underlying image quality. In some instances, this may be accomplished by improving the associated imaging hardware. For example, improvements in sensors as well as optics can yield higher-quality images. However, hardware improvements are not always possible. In some cases, the cost benefit analysis simply rules out the use of better hardware. In some other cases, better hardware is not available. Accordingly, image analysis techniques may be used to provide for enhanced images.
Super resolution (SR) is a technique to generate a higher resolution image or image sequence from a low resolution (noisy) image or image sequence of a scene. Higher resolution image offers a higher pixel density and thereby more details about the original scene. Generally, super resolution (SR) makes use of a series of related images to enhance content within a given image. In simple terms, super resolution (SR) relates one image to related data in a series of subsequent and/or preceding images and improves representations of the image according to the related data.
In many instances, super resolution (SR) can make dramatic improvements to image quality. This is particularly the case where the imaging sensor is stationary relative to a subject, and the subject is substantially static (i.e., there is no movement within the scene that is being imaged).
Unfortunately, such constraints rarely present themselves. For example, in medical imaging, a subject may move some as they shift positions while imaging. In security applications, such as where a sensor is used to image a landscape that includes, for example, people walking on the street, the subjects moved throughout the sequence of images. Additionally, sensors used in applications such as the security application may be subject to vibration, such as from wind buffeting the housing in which the sensor is mounted.
As a result, many image sequences contain global movement (i.e., movement of the sensor relative to the sensing area) as well as local movement (i.e., movement within the sensing area). As a result, efficacy of super resolution (SR) techniques often does not perform nearly as well in real-world conditions when compared to laboratory or test conditions.
Thus, what are needed are improved techniques for super resolution (SR). The techniques should provide for reducing the effect of global motion and local motion upon output images.